Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China

Rapid and accurate identification of tree species via remote sensing technology has become one of the important means for forest inventory. This paper is to develop an accurate tree species identification framework that integrates unmanned airborne vehicle (UAV)-based hyperspectral image and light detection and ranging (LiDAR) data under the complex condition of natural coniferous and broad-leaved mixed forests. First, the UAV-based hyperspectral image and LiDAR data were obtained from a natural coniferous and broad-leaved mixed forest in the Maoer Mountain area of Northeast China. The preprocessed LiDAR data was segmented using a distance-based point cloud clustering algorithm to obtain the point cloud of individual trees; the hyperspectral image was segmented using the projection outlines of individual tree point clouds to obtain the hyperspectral data of individual trees. Then, different hyperspectral and LiDAR features were extracted, respectively, and the importance of the features was analyzed by a random forest (RF) algorithm in order to select appropriate features for the single-source and multi-source data. Finally, tree species identification in the study area were conducted by using a support vector machine (SVM) algorithm together with hyperspectral features, LiDAR features and fused features, respectively. Results showed that the total accuracy for individual tree segmentation was 84.62%, and the fused features achieved the best accuracy for identification of the tree species (total accuracy = 89.20%), followed by the hyperspectral features (total accuracy = 86.08%) and LiDAR features (total accuracy = 76.42%). The optimal features for tree species identification based on fusion of the hyperspectral and LiDAR data included the vegetation indices that were sensitive to the chlorophyll, anthocyanin and carotene contents in the leaves, the partial components of the transformed independent component analysis (ICA), minimum noise fraction (MNF) and principal component analysis (PCA), and the intensity features of the LiDAR echo, respectively. It was concluded that the framework developed in this study was effective in tree species identification under the complex conditions of natural coniferous and broad-leaved mixed forest and the fusion of UAV-based hyperspectral image and LiDAR data can achieve enhanced accuracy compared the single-source UAV-based remote sensing data.

[1]  Cibele Hummel do Amaral,et al.  Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion , 2021 .

[2]  Maitiniyazi Maimaitijiang,et al.  Urban tree species classification using UAV-based multi-sensor data fusion and machine learning , 2021, GIScience & Remote Sensing.

[3]  Li Zhang,et al.  High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data , 2021, Remote. Sens..

[4]  Kai Liu,et al.  Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Qiujie Li,et al.  Street tree segmentation from mobile laser scanning data , 2020 .

[6]  Bin Zhang,et al.  Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images , 2020, Remote Sensing of Environment.

[7]  Pinliang Dong,et al.  Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data , 2020, Remote. Sens..

[8]  Leiguang Wang,et al.  Analyzing the role of spatial features when cooperating hyperspectral and LiDAR data for the tree species classification in a subtropical plantation forest area , 2020 .

[9]  Yong Pang,et al.  Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China , 2020, Forests.

[10]  Hui Lin,et al.  GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong , 2020, Remote. Sens..

[11]  Fabian Ewald Fassnacht,et al.  Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Daniel Schläpfer,et al.  A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography , 2020, Remote. Sens..

[13]  Xiaoli Zhang,et al.  Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data , 2019, Forests.

[14]  Cao Lin,et al.  Effective Feature Extraction and Identification Method Based on Tree Laser Point Cloud , 2019, Chinese Journal of Lasers.

[15]  Janet Franklin,et al.  A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery , 2019, Remote. Sens..

[16]  Michele Dalponte,et al.  Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data , 2019, Remote. Sens..

[17]  Xuehua Liu,et al.  A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment , 2018, Forests.

[18]  Clement Atzberger,et al.  Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data , 2018, Remote. Sens..

[19]  Marco Heurich,et al.  Tree species classification using plant functional traits from LiDAR and hyperspectral data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[20]  A. Skidmore,et al.  Important LiDAR metrics for discriminating forest tree species in Central Europe , 2018 .

[21]  T. Sankey,et al.  UAV hyperspectral and lidar data and their fusion for arid and semi‐arid land vegetation monitoring , 2018 .

[22]  Xin Shen,et al.  Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data , 2017, Remote. Sens..

[23]  Jonathon J. Donager,et al.  UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA , 2017 .

[24]  Lorenzo Bruzzone,et al.  Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images , 2017, IEEE Transactions on Image Processing.

[25]  Yanjun Su,et al.  Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas , 2016 .

[26]  Hao Lu,et al.  LiCHy: The CAF's LiDAR, CCD and Hyperspectral Integrated Airborne Observation System , 2016, Remote. Sens..

[27]  Le Wang,et al.  An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery , 2015 .

[28]  Andreas Ziegler,et al.  Mining data with random forests: current options for real‐world applications , 2014, WIREs Data Mining Knowl. Discov..

[29]  Xiaoye Liu,et al.  Support vector machines for tree species identification using LiDAR-derived structure and intensity variables , 2013 .

[30]  T. Noland,et al.  Classification of tree species based on structural features derived from high density LiDAR data , 2013 .

[31]  Liu Lijuan,et al.  Fused airborne LiDAR and hyperspectral data for tree species identification in a natural temperate forest , 2013, National Remote Sensing Bulletin.

[32]  L. Bruzzone,et al.  Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data , 2012 .

[33]  R. Jensen,et al.  Classification of urban tree species using hyperspectral imagery , 2012 .

[34]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[35]  Nadia Essoussi,et al.  Hyperspectral data classification using geostatistics and support vector machines , 2011 .

[36]  J. Hyyppä,et al.  Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type , 2010 .

[37]  H. Andersen,et al.  Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data , 2009 .

[38]  John A. Richards,et al.  Using Suitable Neighbors to Augment the Training Set in Hyperspectral Maximum Likelihood Classification , 2008, IEEE Geoscience and Remote Sensing Letters.

[39]  Jianwen Luo,et al.  Savitzky-Golay smoothing and differentiation filter for even number data , 2005, Signal Process..

[40]  Nicholas C. Coops,et al.  Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..